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A Conscientious Rival Penalized Competitive Learning Text Clustering Algorithm

  • Mao-ting Gao
  • Zheng-ou Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)

Abstract

Text features are usually expressed as a huge dimensional vector in text mining, LSA can reduce dimensionality of text features effectively, and emerges the semantic relations between texts and terms. This paper presents a Conscientious Rival Penalized Competitive Learning (CRPCL) text clustering algorithm, which uses LSA to reduce the dimensionality and improves RPCL to set a conscientious threshold to restrict a winner that won too many times and to make every neural unit win the competition at near ideal probability. The experiments demonstrate good performance of this method.

Keywords

Weight Vector Text Feature Radial Basis Function Neural Network Latent Semantic Analysis Ideal Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mao-ting Gao
    • 1
    • 2
  • Zheng-ou Wang
    • 1
  1. 1.Institute of Systems EngineeringTianjin UniversityTianjinChina
  2. 2.Computer Science DepartmentShanghai Maritime UniversityShanghaiChina

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